The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum
of squares. The CSS estimates are conditional on the assumption that the past unobserved errors
are 0s. The estimation produced by CSS can be used as a starting point for a better algorithm,
e.g., the maximum likelihood.

Note that the order of integration is taken as an input, not estimated.

Constructor Detail

ConditionalSumOfSquares

Fit an ARIMA model for the observations using CSS. Note that the algorithm fits only an ARMA
model. d is taken as an input. If the differenced input time series is not zero-mean,
it is first de-mean-ed before running the algorithm as in Brockwell and Davis. When reporting
the model, we compute the intercept to match the mean.

Parameters:

x - the time series of observations

p - the number of AR terms

d - the order of integration

q - the number of MA terms

maxIterations - the maximum number of iterations

ConditionalSumOfSquares

public ConditionalSumOfSquares(double[] x,
int p,
int d,
int q)

Fit an ARIMA model for the observations using CSS. Note that the algorithm fits only an ARMA
model. d is taken as an input. If the differenced input time series is not zero-mean,
it is first de-mean-ed before running the algorithm as in Brockwell and Davis. When reporting
the model, we compute the intercept to match the mean.

Parameters:

x - the time series of observations

p - the number of AR terms

d - the order of integration

q - the number of MA terms

Method Detail

nParams

public int nParams()

Get the number of parameters for the estimation/fitting. They are the AR terms, MA terms, and
variance (sigma^2).